Autor:	Sabeti, Elyas, Høst-Madsen, Anders
Rok vydání:	2017
Předmět:	Computer Science - Learning Computer Science - Information Theory
Druh dokumentu:	Working Paper
Popis:	Heart rate variability (HRV) is a vital measure of the autonomic nervous system functionality and a key indicator of cardiovascular condition. This paper proposes a novel method, called pattern tree which is an extension of Willem's context tree to real-valued data, to investigate HRV via an atypicality framework. In a previous paper atypicality was developed as method for mining and discovery in "Big Data," which requires a universal approach. Using the proposed pattern tree as a universal source coder in this framework led to discovery of arrhythmias and unknown patterns in HRV Holter Monitoring. Comment: 5 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1710.07319 Zobrazit plný text záznamu View this record from Arxiv